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Notes by Simulation Model/Codebase

Mark E. Madsen · 2017

Cited by 0

Kwan-Lab/aboharbdavoudian2025: Publication Codebase

Farid Aboharb, PhD · Zenodo (CERN European Organization for Nuclear Research) · 2025

code-base associated with the publication of the associated paper, following revisions made during the submission process.

Cited by 0Open access

Codebase release 1.0 for TorchGPE

Lorenzo Fioroni, Luca Gravina, Justyna Stefaniak et al. · SciPost Physics Codebases · 2024

TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational efficiency by leveraging GPU computing capabilities. With the integration of the latter technology, TorchGPE achieves a substantial speed-up with respect to conventional CPU-based methods, greatly expanding the scope and potential of research in this field.

Cited by 2Open access

Codebase release 4.0 for RIVET

Christian Bierlich, A. G. Buckley, J. M. Butterworth et al. · SciPost Physics Codebases · 2024

The Rivet toolkit is the primary mechanism for phenomenological preservation of collider-physics measurements, containing both a computational core and API for analysis implementation, and a large collection of more than a thousand preserved analyses. In this note we summarise the main changes in the new Rivet 4 major release series. These include a major generalisation and more semantically coherent model for histograms and related data objects, a thorough clean-up of inelegant and legacy observable-computation tools, and new systems for extended analysis-data, incorporation of preserved machine-learning models, and serialization for high-performance computing applications. Where these changes introduce backward-incompatible interface changes, existing analyses have been updated and indications are given on how to update new analysis routines and workflows.

Cited by 3Open access

Codebase release 0.4 for pyBumpHunter

Louis Vaslin, S. Calvet, Vincent Barra et al. · SciPost Physics Codebases · 2023

The BumpHunter algorithm is widely used in the search for new particles in High Energy Physics analysis. This algorithm offers the advantage of evaluating the local and global p-values of a localized deviation in the observed data without making any hypothesis on the supposed signal. The increasing popularity of the Python programming language motivated the development of a new public implementation of this algorithm in Python, called pyBumpHunter, together with several improvements and additional features. It is the first public implementation of the BumpHunter algorithm to be added to Scikit-HEP. This paper presents in detail the BumpHunter algorithm as well as all the features proposed in this implementation. All these features have been tested in order to demonstrate their behaviour and performance.

Cited by 1Open access

Codebase release 0.1 for infstat

Kyoungchul Kong, K. Matchev, S. Mrenna et al. · SciPost Physics Codebases · 2023

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential \varphi <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>φ</mml:mi></mml:math> . In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

Cited by 2Open access

Codebase release r1.4 for CoVVVR

Prasanth Shyamsundar, Jacob L. Scott, S. Mrenna et al. · SciPost Physics Codebases · 2024

Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named COVVVR, which implements our approach in the VEGAS program. The improvements are quantified with several benchmark examples from the literature.

Cited by 1Open access

Codebase release 8.3 for PYTHIA

Christian Bierlich, Smita Chakraborty, Nishita Desai et al. · SciPost Physics Codebases · 2022

This manual describes the Pythia event generator, the most recent version of an evolving physics tool used to answer fundamental questions in particle physics. The program is most often used to generate high-energy-physics collision “events”, i.e. sets of particles produced in association with the collision of two incoming high-energy particles, but has several uses beyond that. The guiding philosophy is to produce and re-produce properties of experimentally obtained collisions as accurately as possible. The program includes a wide ranges of reactions within and beyond the Standard Model, and extending to heavy ion physics. Emphasis is put on phenomena where strong interactions play a major role. The manual contains both pedagogical and practical components. All included physics models are described in enough detail to allow the user to obtain a cursory overview of used assumptions and approximations, enabling an informed evaluation of the program output. A number of the most central algorithms are described in enough detail that the main results of the program can be reproduced independently, allowing further development of existing models or the addition of new ones. Finally, a chapter dedicated fully to the user is included towards the end, providing pedagogical examples of standard use cases, and a detailed description of a number of external interfaces. The program code, the online manual, and the latest version of this print manual can be found on the Pythia web page: https://www.pythia.org/.

Cited by 22Open access

Codebase release 1.3 for SymTensor

Yang Gao, Phillip Helms, Garnet Kin‐Lic Chan et al. · SciPost Physics Codebases · 2023

Tensor contractions are ubiquitous in computational chemistry and physics, where tensors generally represent states or operators and contractions express the algebra of these quantities. In this context, the states and operators often preserve physical conservation laws, which are manifested as group symmetries in the tensors. These group symmetries imply that each tensor has block sparsity and can be stored in a reduced form. For nontrivial contractions, the memory footprint and cost are lowered, respectively, by a linear and a quadratic factor in the number of symmetry sectors. State-of-the-art tensor contraction software libraries exploit this opportunity by iterating over blocks or using general block-sparse tensor representations. Both approaches entail overhead in performance and code complexity. With intuition aided by tensor diagrams, we present a technique, irreducible representation alignment, which enables efficient handling of Abelian group symmetries via only dense tensors, by using contraction-specific reduced forms. This technique yields a general algorithm for arbitrary group symmetric contractions, which we implement in Python and apply to a variety of representative contractions from quantum chemistry and tensor network methods. As a consequence of relying on only dense tensor contractions, we can easily make use of efficient batched matrix multiplication via Intel’s MKL and distributed tensor contraction via the Cyclops library, achieving good efficiency and parallel scalability on up to 4096 Knights Landing cores of a supercomputer.

Cited by 1Open access

Codebase release 3.4 for NetKet

Filippo Vicentini, Damian Hofmann, Attila Szabó et al. · SciPost Physics Codebases · 2022

We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural quantum states and provides efficient algorithms for their evaluation and optimization. This new version is built on top of JAX, a differentiable programming and accelerated linear algebra framework for the Python programming language. The most significant new feature is the possibility to define arbitrary neural network ansätze in pure Python code using the concise notation of machine-learning frameworks, which allows for just-in-time compilation as well as the implicit generation of gradients thanks to automatic differentiation. NetKet 3 also comes with support for GPU and TPU accelerators, advanced support for discrete symmetry groups, chunking to scale up to thousands of degrees of freedom, drivers for quantum dynamics applications, and improved modularity, allowing users to use only parts of the toolbox as a foundation for their own code.

Cited by 7Open access

Codebase release 1.0 for Cytnx

Kai-Hsin Wu, Chang-Teng Lin, Ke Hsu et al. · SciPost Physics Codebases · 2025

We introduce a tensor network library designed for classical and quantum physics simulations called Cytnx (pronounced as sci-tens). This library provides an almost identical interface and syntax for both C++ and Python, allowing users to effortlessly switch between the two languages. Aiming at a quick learning process for new users of tensor network algorithms, the interfaces resemble the popular Python scientific libraries like NumPy, Scipy, and PyTorch. Not only multiple global Abelian symmetries can be easily defined and implemented, Cytnx also provides a new tool called Network that allows users to store large tensor networks and perform tensor network contractions in an optimal order automatically. With the integration of cuQuantum, tensor calculations can also be executed efficiently on GPUs. We present benchmark results for tensor operations on both devices, CPU and GPU. We also discuss features and higher-level interfaces to be added in the future.

Cited by 1Open access

Codebase release 1.3 for QArray

Barnaby van Straaten, Joseph Hickie, Lucas Schorling et al. · SciPost Physics Codebases · 2024

Semiconductor quantum dot arrays are a leading architecture for the development of quantum technologies. Over the years, the constant capacitance model has served as a fundamental framework for simulating, understanding, and navigating the charge stability diagrams of small quantum dot arrays. However, while the size of the arrays keeps growing, solving the constant capacitance model becomes computationally prohibitive. This paper presents an open-source software package able to compute a 100 × 100 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mn>100</mml:mn><mml:mo>×</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:math> pixels charge stability diagram of a 16-dot array in less than a second. Smaller arrays can be simulated in milliseconds - faster than they could be measured experimentally, enabling the creation of diverse datasets for training machine learning models and the creation of digital twins that can interface with quantum dot devices in real-time. Our software package implements its core functionalities in the systems programming language Rust and the high-performance numerical computing library JAX. The Rust implementation benefits from advanced optimisations and parallelisation, enabling the users to take full advantage of multi-core processors. The JAX implementation allows for GPU acceleration.

Cited by 4Open access

Codebase release 1.0 for TeNPy

Johannes Hauschild, Jakob Unfried, Sajant Anand et al. · SciPost Physics Codebases · 2024

TeNPy (short for ‘Tensor Network Python’) is a python library for the simulation of strongly correlated quantum systems with tensor networks. The philosophy of this library is to achieve a balance of readability and usability for new-comers, while at the same time providing powerful algorithms for experts. The focus is on MPS algorithms for 1D and 2D lattices, such as DMRG ground state search, as well as dynamics using TEBD, TDVP, or MPO evolution. This article is a companion to the recent version 1.0 release of TeNPy and gives a brief overview of the package.

Cited by 7Open access

Codebase release 1.0 for LEMONS

Oscar Dufour, Maxime Stapelle, Alexandre Nicolas · SciPost Physics Codebases · 2026

To model dense crowds, the usual recourse to oversimplified (circular) pedestrian shapes and contact forces shows limitations. To help modellers overcome these limitations, we propose an open-source numerical tool. It consists of a Python library that generates 2D and 3D pedestrian crowds based on anthropometric data, and a C++ library that computes mechanical contacts with other agents and with obstacles, and evolves the crowd’s configuration. Additionally, we provide an online platform with a user-friendly graphical interface for the Python library, and scripts to call the C++ library from Python. The tool enables users to implement their own decisional layer, i.e., to control the agents’ choices of desired velocities.

Cited by 1Open access

Codebase for Balcony Photovoltaics Research

Feinan, Lyu · Zenodo (CERN European Organization for Nuclear Research) · 2026

Cited by 0Open access

Divjoy - The React Codebase Generator

Björn Stierand · 2019

Cited by 0

Codebase release 2.0 for sauce

Caleb Marshall · SciPost Physics Codebases · 2024

Low energy nuclear physics experiments are transitioning towards fully digital data acquisition systems. Realizing the gains in flexibility afforded by these systems relies on equally flexible data reduction techniques. In this paper, methods utilizing data frames and in-memory techniques to work with data, including data from self-triggering, digital data acquisition systems, are discussed within the context of a Python package, sauce. It is shown that data frame operations can encompass common analysis needs and allow interactive data analysis. Two event building techniques, dubbed referenced and referenceless event building, are shown to provide a means to transform raw list mode data into correlated multi-detector events. These techniques are demonstrated in the analysis of two example data sets.

Cited by 1Open access

Codebase release 4.0 for QSpace

Andreas Weichselbaum · SciPost Physics Codebases · 2024

This is the documentation for the tensor library QSpace (v4.0) that provides a toolbox to exploit ‘quantum symmetry spaces’ in tensor network states in the quantum many-body context. QSpace permits arbitrary combinations of symmetries including the Abelian symmetries \mathbb{Z}_n <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mstyle mathvariant="double-struck"><mml:mi>ℤ</mml:mi></mml:mstyle><mml:mi>n</mml:mi></mml:msub></mml:math> and U(1) <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>U</mml:mi><mml:mo stretchy="false" form="prefix">(</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy="false" form="postfix">)</mml:mo></mml:mrow></mml:math> , as well as all non-Abelian symmetries represented by the simple classical Lie algebras: A_n <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>A</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:math> , B_n <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>B</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:math> , C_n <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>C</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:math> , and D_n <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>D</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:math> , or respectively, the special unitary group SU( n <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>n</mml:mi></mml:math> ), the odd orthogonal group SO( 2n+1 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mn>2</mml:mn><mml:mi>n</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math> ), the symplectic group Sp( 2n <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:mrow></mml:math> ), and the even orthogonal group SO( 2n <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:mrow></mml:math> ). The code (C++ embedded via the MEX interface into Matlab) is available (https://bitbucket.org/qspace4u) under the Apache 2.0 license. QSpace is designed as a bottom-up approach for non-Abelian symmetries. It starts from the defining representation and the respective Lie algebra. By explicitly computing and tabulating generalized Clebsch-Gordan coefficient tensors, QSpace is versatile in its operations across all symmetries. At the level of an application, much of the symmetry-related details are hidden within the QSpace C++ core libraries. Hence when developing tensor network algorithms with QSpace, these can be coded (nearly) as if there are no symmetries at all, despite being able to fully exploit general non-Abelian symmetries.

Cited by 5Open access

Codebase release 1.1 for TASEPy

Luca Ciandrini, Richmond L. Crisostomo, Juraj Szavits-Nossan · SciPost Physics Codebases · 2023

The totally asymmetric simple exclusion process (TASEP) is a paradigmatic lattice model for one-dimensional particle transport subject to excluded-volume interactions. Solving the inhomogeneous TASEP in which particles’ hopping rate vary across the lattice is a long-standing problem. In recent years, a power series approximation (PSA) has been developed to tackle this problem, however no computer algorithm currently exists that implements this approximation. This paper addresses this issue by providing a Python-based package TASEPy that finds the steady state solution of the inhomogeneous TASEP for any set of hopping rates using the PSA truncated at a user-defined order.

Cited by 1Open access

Codebase release 0.5 for divERGe

Jonas B. Profe, Dante M. Kennes, Lennart Klebl · SciPost Physics Codebases · 2024

We present divERGe, an open source, high-performance C/C++/Python library for functional renormalization group (FRG) calculations on lattice fermions. The versatile model interface is tailored to real materials applications and seamlessly integrates with existing, standard tools from the ab-initio community. The code fully supports multi-site, multi-orbital, and non-SU(2) models in all of the three included FRG variants: TU ^2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msup><mml:mi/><mml:mn>2</mml:mn></mml:msup></mml:math> FRG, N-patch FRG, and grid FRG. With this, the divERGe library paves the way for widespread application of FRG as a tool in the study of competing orders in quantum materials.

Cited by 3Open access